EconPapers    
Economics at your fingertips  
 

A cost-sensitive ensemble deep forest approach for extremely imbalanced credit fraud detection

Fang Zhao, Gang Li, Yanxia Lyu, Hongdong Ma and Xiaoqian Zhu

Quantitative Finance, 2023, vol. 23, issue 10, 1397-1409

Abstract: Credit fraud detection modeling helps prevent default risks and reduce economic losses, and increasingly sophisticated methods have been designed for predicting the default probability of clients. In such problems, the fact that the class of fraud clients is much smaller than the class of good clients makes it a challenge to detect the fraud class. To minimize the financial losses in extremely imbalanced datasets, this paper delivers a novel cost-sensitive ensemble model under the framework of deep forest. The model first introduces a cost-sensitive strategy to assign a higher cost to the fraud class, thereby improving the attention of the model to the fraud samples. As everyone knows, for the basic classifiers of ensemble learning, the greater their differences, the better the performance after ensemble. So the model adds superior cost-sensitive base classifiers into the cascade structure to improve the overall performance. The model also introduces Type II error as the convergence index to automatically adjust the depth of the cascade structure. The experiments conducted on the European credit dataset and a private electronic transaction dataset are presented to demonstrate the performance of the proposed method. The results indicate that the proposed model outperforms most benchmarks in detecting fraud samples.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2023.2230264 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:23:y:2023:i:10:p:1397-1409

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2023.2230264

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:quantf:v:23:y:2023:i:10:p:1397-1409